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Legacy Data Management Slows Real-Time Analytics Adoption

A recent survey says that legacy data management and transformation solutions impede the use of real-time analytics in healthcare.

Legacy data management solutions slow down real-time analytics adoption.

Source: Thinkstock

By Elizabeth O'Dowd

- Healthcare organizations that are trying to make better use of data through real-time analytics are struggling with data management as legacy methods are not robust enough to support more advanced data processing.

A recent study conducted by IDC and InterSystems found that organizations using extract, transfer, and load (ETL) and Changed Data Capture (CDC) are largely unable to keep up with the demands of real-time data analytics. The report surveyed over 500 enterprise level organizations across all major verticals including healthcare.

"As organizations look to compete and accelerate innovation, this study highlights the importance of concurrent transaction processing and real-time data analytics for improving customer experience, business productivity, and operations,” InterSystems Vice President Paul Grabscheid said in a statement.

ETL is when data is extracted from a similar data source. The data is then transformed to a compatible format for storage and analytics. Lastly, data is loaded into the database or data warehouse.

CDC is a group of software design patterns that tracks data that has changed so the alterations can be identified for data integration purposes.

The study found that real-time data analytics is becoming increasingly significant as organizations collect and store more data.

Seventy-five percent of respondents believe that untimely data has negatively impacted business opportunities. Twenty-seven percent said that it also negatively impacted productivity and workflow. Fifty-four percent indicated that untimely data limited operational efficiencies.

These results were largely impacted by legacy data integration and management techniques ETL and CDC. The study found that nearly two-thirds of the data moved using ETL was five days old by the time it reached the analytics database. 

CDC takes an average of 10 minutes to move 65 percent of the data to the analytics data base, according to the survey. This makes it very difficult for clinicians to use live data, which often means that the patient needs to come back for results and costs organizations time and money.

Report authors found it alarming that data management and transformation was slow overall considering how important respondents believed all new data types to be. Relational, Internet of Things (IoT) data, streaming data from external sources, sensor data, graphs, key value, video/audio/image, object, JSON documents and geospatial data were all considered very important by most of the organizations surveyed.

IoT data is especially significant to healthcare real-time analytics and can help clinicians gain more insight into a patient’s condition at the point of care.

Real-time data lets clinicians collect, analyze, and decide on a patient’s condition during the initial interaction. Real-time environments lower costs by avoiding the bulk processing and the overnight loading into data warehouses.

Real-time environments also help with data governance, making sure the information entered is correct. If organizations can address data governance upfront, it solves a lot of problems concerning data quality.

However, many organizations still struggle with integrating data from a vast amount of resources. The introduction of IoT devices into healthcare networks, along with the various records that may belong to each individual patient, make it difficult to gain a holistic view of a patient’s medical history.

Legacy data management solutions also prevent clinicians from embracing real-time analytics because it takes too long to collect, process, and transform the data. Real-time analytics saves organizations money and improves the quality of care because patients can get their results during their initial appointment and don’t need to come back for as many follow ups.

Real-time solutions are capable of connecting clinicians around the world and support collaboration. However, healthcare organizations need to solve data management issues to take full advantage of the technology so clinicians have access to all of a patient’s information at the point of care. 


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